Overview

Brought to you by YData

Dataset statistics

Number of variables9
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows163
Duplicate rows (%)1.6%
Total size in memory703.3 KiB
Average record size in memory72.0 B

Variable types

Numeric9

Alerts

Dataset has 163 (1.6%) duplicate rowsDuplicates
area_total is highly overall correlated with m2totalHigh correlation
desconto is highly overall correlated with precoHigh correlation
m2total is highly overall correlated with area_totalHigh correlation
preco is highly overall correlated with desconto and 1 other fieldsHigh correlation
valor_de_avaliacao is highly overall correlated with precoHigh correlation
area_total is highly skewed (γ1 = 21.29624356) Skewed
area_privativa is highly skewed (γ1 = 84.03173293) Skewed
area_terreno is highly skewed (γ1 = 81.95531079) Skewed
m2total is highly skewed (γ1 = 70.69911621) Skewed
m2terreno is highly skewed (γ1 = 29.68065005) Skewed
desconto has 278 (2.8%) zeros Zeros
area_total has 8114 (81.1%) zeros Zeros
area_privativa has 976 (9.8%) zeros Zeros
area_terreno has 2675 (26.8%) zeros Zeros
m2total has 8114 (81.1%) zeros Zeros
m2privativa has 976 (9.8%) zeros Zeros
m2terreno has 2675 (26.8%) zeros Zeros

Reproduction

Analysis started2025-11-05 21:34:34.643805
Analysis finished2025-11-05 21:34:38.230886
Duration3.59 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

preco
Real number (ℝ)

High correlation 

Distinct5420
Distinct (%)54.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77190.027
Minimum4988
Maximum6807791
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-11-05T22:34:38.271369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4988
5-th percentile22910.05
Q138513.75
median51591
Q366976
95-th percentile182909
Maximum6807791
Range6802803
Interquartile range (IQR)28462.25

Descriptive statistics

Standard deviation172483.47
Coefficient of variation (CV)2.2345305
Kurtosis488.15531
Mean77190.027
Median Absolute Deviation (MAD)14018
Skewness17.69528
Sum7.7190027 × 108
Variance2.9750547 × 1010
MonotonicityNot monotonic
2025-11-05T22:34:38.317589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60000 43
 
0.4%
52103 42
 
0.4%
57892 42
 
0.4%
62857 39
 
0.4%
49187 35
 
0.4%
65922 35
 
0.4%
68788 34
 
0.3%
51236 30
 
0.3%
75306 30
 
0.3%
55946 29
 
0.3%
Other values (5410) 9641
96.4%
ValueCountFrequency (%)
4988 1
< 0.1%
5354 1
< 0.1%
5785 1
< 0.1%
6245 1
< 0.1%
6330 1
< 0.1%
6383 1
< 0.1%
7174 1
< 0.1%
7253 1
< 0.1%
7263 1
< 0.1%
7477 1
< 0.1%
ValueCountFrequency (%)
6807791 1
< 0.1%
5485929 1
< 0.1%
5386917 1
< 0.1%
3276723 1
< 0.1%
3223106 1
< 0.1%
3200000 1
< 0.1%
2750000 1
< 0.1%
2716560 1
< 0.1%
2408075 1
< 0.1%
2377182 1
< 0.1%

valor_de_avaliacao
Real number (ℝ)

High correlation 

Distinct2219
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean161444.02
Minimum10750
Maximum10682000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-11-05T22:34:38.363553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10750
5-th percentile70000
Q1101000
median123000
Q3147617
95-th percentile310000
Maximum10682000
Range10671250
Interquartile range (IQR)46617

Descriptive statistics

Standard deviation284692.76
Coefficient of variation (CV)1.7634147
Kurtosis474.90478
Mean161444.02
Median Absolute Deviation (MAD)23000
Skewness17.972708
Sum1.6144402 × 109
Variance8.1049966 × 1010
MonotonicityNot monotonic
2025-11-05T22:34:38.411998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120000 234
 
2.3%
130000 205
 
2.1%
125000 196
 
2.0%
110000 196
 
2.0%
100000 184
 
1.8%
140000 175
 
1.8%
135000 166
 
1.7%
115000 165
 
1.7%
105000 152
 
1.5%
150000 134
 
1.3%
Other values (2209) 8193
81.9%
ValueCountFrequency (%)
10750 1
 
< 0.1%
14500 1
 
< 0.1%
14600 1
 
< 0.1%
18000 1
 
< 0.1%
18500 1
 
< 0.1%
20000 5
0.1%
21164 2
 
< 0.1%
21500 1
 
< 0.1%
22000 3
< 0.1%
22397 1
 
< 0.1%
ValueCountFrequency (%)
10682000 1
< 0.1%
9226000 1
< 0.1%
8984500 1
< 0.1%
6504800 1
< 0.1%
5500000 1
< 0.1%
5187000 1
< 0.1%
5168000 1
< 0.1%
4400000 1
< 0.1%
4261000 1
< 0.1%
4250000 1
< 0.1%

desconto
Real number (ℝ)

High correlation  Zeros 

Distinct62
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.5387
Minimum0
Maximum90
Zeros278
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-11-05T22:34:38.459213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile36
Q143
median56
Q367
95-th percentile79
Maximum90
Range90
Interquartile range (IQR)24

Descriptive statistics

Standard deviation16.039571
Coefficient of variation (CV)0.28879989
Kurtosis1.9409832
Mean55.5387
Median Absolute Deviation (MAD)13
Skewness-0.85960075
Sum555387
Variance257.26783
MonotonicityNot monotonic
2025-11-05T22:34:38.504416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42 933
 
9.3%
59 747
 
7.5%
43 630
 
6.3%
56 408
 
4.1%
67 365
 
3.6%
58 310
 
3.1%
0 278
 
2.8%
53 275
 
2.8%
75 273
 
2.7%
61 267
 
2.7%
Other values (52) 5514
55.1%
ValueCountFrequency (%)
0 278
2.8%
1 2
 
< 0.1%
15 1
 
< 0.1%
16 1
 
< 0.1%
24 1
 
< 0.1%
27 1
 
< 0.1%
30 1
 
< 0.1%
33 1
 
< 0.1%
36 221
2.2%
37 62
 
0.6%
ValueCountFrequency (%)
90 1
 
< 0.1%
88 1
 
< 0.1%
87 5
 
0.1%
86 1
 
< 0.1%
85 10
 
0.1%
84 39
 
0.4%
83 10
 
0.1%
82 112
1.1%
81 119
1.2%
80 73
0.7%

area_total
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct214
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.591
Minimum0
Maximum2925
Zeros8114
Zeros (%)81.1%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-11-05T22:34:38.652905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile79
Maximum2925
Range2925
Interquartile range (IQR)0

Descriptive statistics

Standard deviation53.266207
Coefficient of variation (CV)3.4164715
Kurtosis960.26882
Mean15.591
Median Absolute Deviation (MAD)0
Skewness21.296244
Sum155910
Variance2837.2888
MonotonicityNot monotonic
2025-11-05T22:34:38.695152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8114
81.1%
70 81
 
0.8%
58 74
 
0.7%
56 59
 
0.6%
48 53
 
0.5%
50 53
 
0.5%
47 51
 
0.5%
62 51
 
0.5%
69 49
 
0.5%
66 47
 
0.5%
Other values (204) 1368
 
13.7%
ValueCountFrequency (%)
0 8114
81.1%
1 1
 
< 0.1%
8 1
 
< 0.1%
22 1
 
< 0.1%
24 1
 
< 0.1%
26 4
 
< 0.1%
27 2
 
< 0.1%
28 2
 
< 0.1%
29 7
 
0.1%
31 1
 
< 0.1%
ValueCountFrequency (%)
2925 1
< 0.1%
1320 1
< 0.1%
1000 1
< 0.1%
701 1
< 0.1%
695 1
< 0.1%
690 1
< 0.1%
666 1
< 0.1%
654 1
< 0.1%
597 1
< 0.1%
580 1
< 0.1%

area_privativa
Real number (ℝ)

Skewed  Zeros 

Distinct336
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.704
Minimum0
Maximum41700
Zeros976
Zeros (%)9.8%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-11-05T22:34:38.744045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q147
median57
Q368
95-th percentile124
Maximum41700
Range41700
Interquartile range (IQR)21

Descriptive statistics

Standard deviation444.55324
Coefficient of variation (CV)6.287526
Kurtosis7730.2131
Mean70.704
Median Absolute Deviation (MAD)10
Skewness84.031733
Sum707040
Variance197627.58
MonotonicityNot monotonic
2025-11-05T22:34:38.792113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 976
 
9.8%
50 351
 
3.5%
70 348
 
3.5%
58 311
 
3.1%
56 285
 
2.9%
61 278
 
2.8%
48 272
 
2.7%
55 270
 
2.7%
60 267
 
2.7%
47 265
 
2.6%
Other values (326) 6377
63.8%
ValueCountFrequency (%)
0 976
9.8%
22 12
 
0.1%
24 19
 
0.2%
25 3
 
< 0.1%
26 13
 
0.1%
27 4
 
< 0.1%
28 9
 
0.1%
29 17
 
0.2%
30 10
 
0.1%
31 6
 
0.1%
ValueCountFrequency (%)
41700 1
< 0.1%
10480 1
< 0.1%
7200 1
< 0.1%
4825 1
< 0.1%
2390 1
< 0.1%
2003 1
< 0.1%
2002 1
< 0.1%
2000 1
< 0.1%
1612 1
< 0.1%
1546 1
< 0.1%

area_terreno
Real number (ℝ)

Skewed  Zeros 

Distinct813
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean750.9627
Minimum0
Maximum2035960
Zeros2675
Zeros (%)26.8%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-11-05T22:34:38.824103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median143
Q3200
95-th percentile600
Maximum2035960
Range2035960
Interquartile range (IQR)200

Descriptive statistics

Standard deviation22084.797
Coefficient of variation (CV)29.408647
Kurtosis7326.0849
Mean750.9627
Median Absolute Deviation (MAD)87.5
Skewness81.955311
Sum7509627
Variance4.8773824 × 108
MonotonicityNot monotonic
2025-11-05T22:34:38.886838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2675
26.8%
200 783
 
7.8%
180 553
 
5.5%
360 255
 
2.5%
125 245
 
2.5%
150 207
 
2.1%
250 160
 
1.6%
300 150
 
1.5%
160 135
 
1.4%
148 116
 
1.2%
Other values (803) 4721
47.2%
ValueCountFrequency (%)
0 2675
26.8%
1 11
 
0.1%
2 2
 
< 0.1%
14 1
 
< 0.1%
15 1
 
< 0.1%
17 1
 
< 0.1%
21 1
 
< 0.1%
22 1
 
< 0.1%
24 3
 
< 0.1%
26 5
 
0.1%
ValueCountFrequency (%)
2035960 1
< 0.1%
715100 1
< 0.1%
252687 1
< 0.1%
144779 1
< 0.1%
135535 1
< 0.1%
125000 1
< 0.1%
112000 1
< 0.1%
108900 1
< 0.1%
102565 1
< 0.1%
95000 1
< 0.1%

m2total
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct852
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean176.3189
Minimum0
Maximum80000
Zeros8114
Zeros (%)81.1%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-11-05T22:34:38.925587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1084
Maximum80000
Range80000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation898.0182
Coefficient of variation (CV)5.0931477
Kurtosis6246.8679
Mean176.3189
Median Absolute Deviation (MAD)0
Skewness70.699116
Sum1763189
Variance806436.69
MonotonicityNot monotonic
2025-11-05T22:34:38.984060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8114
81.1%
1132 14
 
0.1%
1195 10
 
0.1%
1054 10
 
0.1%
1085 9
 
0.1%
620 9
 
0.1%
1146 8
 
0.1%
1176 8
 
0.1%
1171 8
 
0.1%
1038 8
 
0.1%
Other values (842) 1802
 
18.0%
ValueCountFrequency (%)
0 8114
81.1%
15 1
 
< 0.1%
26 1
 
< 0.1%
31 1
 
< 0.1%
34 1
 
< 0.1%
96 1
 
< 0.1%
108 1
 
< 0.1%
111 1
 
< 0.1%
120 2
 
< 0.1%
122 1
 
< 0.1%
ValueCountFrequency (%)
80000 1
< 0.1%
9920 1
< 0.1%
6759 1
< 0.1%
5921 1
< 0.1%
5821 1
< 0.1%
5634 1
< 0.1%
4717 1
< 0.1%
4259 1
< 0.1%
3722 1
< 0.1%
3651 1
< 0.1%

m2privativa
Real number (ℝ)

Zeros 

Distinct999
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean758.8103
Minimum0
Maximum1209
Zeros976
Zeros (%)9.8%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-11-05T22:34:39.021334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1575
median823
Q31038
95-th percentile1174
Maximum1209
Range1209
Interquartile range (IQR)463

Descriptive statistics

Standard deviation341.2808
Coefficient of variation (CV)0.44975774
Kurtosis-0.02995972
Mean758.8103
Median Absolute Deviation (MAD)226
Skewness-0.86942071
Sum7588103
Variance116472.59
MonotonicityIncreasing
2025-11-05T22:34:39.069204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 976
 
9.8%
1146 31
 
0.3%
1205 30
 
0.3%
1056 26
 
0.3%
1132 26
 
0.3%
934 25
 
0.2%
1185 25
 
0.2%
1143 25
 
0.2%
786 24
 
0.2%
1038 24
 
0.2%
Other values (989) 8788
87.9%
ValueCountFrequency (%)
0 976
9.8%
5 1
 
< 0.1%
9 1
 
< 0.1%
48 1
 
< 0.1%
56 1
 
< 0.1%
65 1
 
< 0.1%
66 1
 
< 0.1%
74 2
 
< 0.1%
77 1
 
< 0.1%
118 1
 
< 0.1%
ValueCountFrequency (%)
1209 2
 
< 0.1%
1208 15
0.1%
1207 19
0.2%
1206 10
 
0.1%
1205 30
0.3%
1204 11
 
0.1%
1203 13
0.1%
1202 13
0.1%
1201 11
 
0.1%
1200 18
0.2%

m2terreno
Real number (ℝ)

Skewed  Zeros 

Distinct1185
Distinct (%)11.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean336.7037
Minimum0
Maximum77046
Zeros2675
Zeros (%)26.8%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-11-05T22:34:39.120476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median227
Q3396
95-th percentile814.05
Maximum77046
Range77046
Interquartile range (IQR)396

Descriptive statistics

Standard deviation1588.2752
Coefficient of variation (CV)4.7171302
Kurtosis1028.1151
Mean336.7037
Median Absolute Deviation (MAD)196
Skewness29.68065
Sum3367037
Variance2522618.1
MonotonicityNot monotonic
2025-11-05T22:34:39.169531image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2675
 
26.8%
180 34
 
0.3%
261 29
 
0.3%
272 28
 
0.3%
232 27
 
0.3%
289 26
 
0.3%
210 26
 
0.3%
265 25
 
0.2%
309 25
 
0.2%
283 24
 
0.2%
Other values (1175) 7081
70.8%
ValueCountFrequency (%)
0 2675
26.8%
1 5
 
0.1%
2 4
 
< 0.1%
3 5
 
0.1%
4 2
 
< 0.1%
5 2
 
< 0.1%
6 1
 
< 0.1%
7 2
 
< 0.1%
8 3
 
< 0.1%
9 6
 
0.1%
ValueCountFrequency (%)
77046 1
< 0.1%
50212 1
< 0.1%
49787 1
< 0.1%
42896 1
< 0.1%
42154 1
< 0.1%
41238 1
< 0.1%
40664 1
< 0.1%
39759 1
< 0.1%
35469 1
< 0.1%
34083 1
< 0.1%

Interactions

2025-11-05T22:34:37.759871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-05T22:34:34.746289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-05T22:34:35.100926image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-05T22:34:35.529666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-05T22:34:35.860824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-05T22:34:36.221542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-05T22:34:36.560812image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-05T22:34:36.942689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-05T22:34:37.406295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-05T22:34:37.801763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-05T22:34:34.788933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-05T22:34:35.140235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-05T22:34:35.565986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-05T22:34:35.899007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-05T22:34:36.257213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-05T22:34:36.599184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-05T22:34:36.981542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-05T22:34:37.446220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-05T22:34:37.842119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-05T22:34:34.827610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-05T22:34:35.177474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-05T22:34:35.602200image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-05T22:34:35.934510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-05T22:34:36.295520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-05T22:34:37.716092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-11-05T22:34:39.206177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
area_privativaarea_terrenoarea_totaldescontom2privativam2terrenom2totalprecovalor_de_avaliacao
area_privativa1.0000.1190.171-0.1260.1740.2960.1410.3340.280
area_terreno0.1191.0000.064-0.289-0.2770.3500.0630.2670.059
area_total0.1710.0641.000-0.2640.1330.1680.9850.128-0.055
desconto-0.126-0.289-0.2641.000-0.275-0.373-0.282-0.6730.090
m2privativa0.174-0.2770.133-0.2751.0000.0590.1670.3740.123
m2terreno0.2960.3500.168-0.3730.0591.0000.1770.3620.087
m2total0.1410.0630.985-0.2820.1670.1771.0000.137-0.069
preco0.3340.2670.128-0.6730.3740.3620.1371.0000.569
valor_de_avaliacao0.2800.059-0.0550.0900.1230.087-0.0690.5691.000

Missing values

2025-11-05T22:34:38.148229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-05T22:34:38.193055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

precovalor_de_avaliacaodescontoarea_totalarea_privativaarea_terrenom2totalm2privativam2terreno
099222.0250000.060.00.00.01900.00.00.052.0
171535.0150000.052.00.00.0600.00.00.0119.0
271535.0150000.052.00.00.0600.00.00.0119.0
371535.0150000.052.00.00.0600.00.00.0119.0
45386917.09226000.042.00.00.049338.00.00.0109.0
578557.0130000.040.00.00.0277.00.00.0284.0
6427001.0670000.036.00.00.01307.00.00.0327.0
7197567.0310000.036.00.00.0630.00.00.0314.0
8344150.0540000.036.00.00.01350.00.00.0255.0
91534001.0560000.00.00.00.0599.00.00.02561.0
precovalor_de_avaliacaodescontoarea_totalarea_privativaarea_terrenom2totalm2privativam2terreno
999071276.0124339.043.059.059.0228.01208.01208.0313.0
999154366.0139000.061.00.045.00.00.01208.00.0
999249541.0107000.054.00.041.00.00.01208.00.0
999349541.0107000.054.00.041.00.00.01208.00.0
999459185.098000.040.00.049.00.00.01208.00.0
999595469.0198000.052.00.079.0110.00.01208.0868.0
999677310.0139000.044.00.064.0244.00.01208.0317.0
999759186.0118000.050.00.049.049.00.01208.01208.0
9998122108.0194000.037.0101.0101.0155.01209.01209.0788.0
999962859.0150000.058.00.052.00.00.01209.00.0

Duplicate rows

Most frequently occurring

precovalor_de_avaliacaodescontoarea_totalarea_privativaarea_terrenom2totalm2privativam2terreno# duplicates
122705.056000.059.00.00.0126.00.00.0180.016
1027824.022880.00.00.00.0250.00.00.0111.06
1127880.0108000.074.045.024.00.0620.01162.00.06
826791.093000.071.045.024.00.0595.01116.00.05
1228913.092000.069.045.024.00.0643.01205.00.04
4947137.0115000.059.00.046.00.00.01025.00.04
6149187.0120000.059.00.045.00.00.01093.00.04
1934855.060000.042.00.00.0125.00.00.0279.03
3241943.0110000.062.00.046.00.00.0912.00.03
5648367.0118000.059.00.045.00.00.01075.00.03